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AI Innovations in Heart Disease Prediction using Machine Learning and Risk Stratification using Deep Learning 14

AI Innovations in Heart Disease Prediction using Machine Lea...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Rubasri, S. Roshini, M. Hemalatha, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331531935

The goal of the heart disease prediction project is to use machine learning and deep learning techniques to create an accurate and efficient system. To obtain insights into the dataset, the project starts with thorough data preprocessing, which includes data cleansing, addressing missing values, and conducting exploratory data analysis. Data visualisation tools are used to support this research in order to find important patterns and correlations that help with the model-building *** combining several decision trees, the method increases the predictive potential of the model. To guarantee robustness and dependability, performance is assessed using important measures like accuracy, precision, recall, and cross-validation. A deep learning algorithm is also included to manage data on heart illness that is based on images. Convolutional neural networks (CNNs) use the Lenet architecture for image classification, and pre-processing and data augmentation approaches improve model *** comparison to conventional approaches, this work suggests a sophisticated heart disease prediction model that combines Machine Learning (ML) and Deep Learning (DL) techniques, attaining an accuracy of X% and greatly lowering misclassification rates. By employing oversampling approaches, the suggested system effectively manages unbalanced datasets and enhances prediction robustness through feature selection and ensemble learning. © 2025 IEEE.

关键词： Convolutional neural networks

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A Cost-Sensitive LSTM Model for Driving Risk Assessment from Vehicular Trajectory Data 100

A Cost-Sensitive LSTM Model for Driving Risk Assessment from...

引用

100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Osho Pranay Kumar, Pradeep Chakraborty, Suchetana Indian Institute of Technology Jodhpur Department of Computer Science and Engineering India

ISBN: (纸本)9798331517786

Identifying risky driving behavior is crucial for early hazard detection, encouraging safer driving practices, and minimizing accident risks. Driving patterns, characterized by sensory data, can be used to classify risky behavior. However, effective classification into risk categories relies on supervised learning methods that require labeled data. The challenge lies in the high cost and difficulty of obtaining accurate groundtruth labels for these signatures. As a result, most datasets lack risk labels. Additionally, because risky incidents are infrequent during regular driving, the dataset collected from studies becomes imbalanced, containing fewer instances of risky events. This imbalance poses a significant challenge, as it biases the model towards the majority class, increasing the likelihood of costly misclassifications where risky instances are incorrectly identified as safe. To address this, we propose a three-stage method. First, we identify driving events indicative of risky behavior from the trajectory data and mathematically formulate them as potential risk indicators. Using these indicators, we then employ clustering to assign appropriate risk labels to the data. Finally, we tackle the class imbalance problem using a cost-sensitive LSTM model that combines a custom loss function with LSTM architecture to prioritize accurately classifying risky instances. Our method outperforms other state-of-the-art approaches with high accuracy, precision, F1 score, and recall of 98.13%, 95.2%, 96.6%, and 96.35%, respectively, effectively managing an imbalanced dataset. © 2024 IEEE.

关键词： Risk assessment

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Enhancing Security in Logic-Locked Systems Through UART-Based Key Authentication

Enhancing Security in Logic-Locked Systems Through UART-Base...

引用

2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jaisachin, B. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

The security of integrated circuits is crucial due to their wide range of applications in modern gadgets. An unauthorized access (i.e., third-party access) or a security breach in the logic-locked system can pose a great threat to user privacy, thus leading to data theft and misusing the user data. Several logic locking methods have been proposed, but with the advancements of technology, the latest attack techniques can easily crack those existing locking techniques. Thus, in this research paper we have proposed a sensor-based methodology to detect the unauthorized access with microswitch and UART. In a globalized world where the security of logic-locked systems is really important, this paper empathizes with UART-based mechanisms as a practical solution. By focusing on the existing logic locking methodologies, this paper contributes to the ongoing security efforts for a secured logic locked system. © 2025 IEEE.

关键词： computer circuits

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A Survey on Recent Developments in Software Vulnerability Detection 1

A Survey on Recent Developments in Software Vulnerability De...

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Rajput, Sachin Kumar, Kuldeep National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350365016

In recent years, the rise of cyber threats and attacks has led to significant financial losses for software industries and individuals worldwide. Early detection of software vulnerabilities is crucial for mitigating future monetary risks. As a result, deep learning methodologies, such as pre-trained models, graph neural networks, transformer-based models, convolutional neural networks, and recurrent neural networks, have gained prominence for their ability to learn features and respond to new data. This paper provides a detailed discussion of recent deep learning methodologies for software vulnerability detection, delving into their approaches, limitations, and employed metrics. The key findings of the review highlight the predominant focus on the National Vulnerability Database, and similar repositories of open-source code, potential challenges in performance when confronted with real-world data, the limited scope of research on types, and the opportunity for exploring multilingual vulnerability detection methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

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"Event Evolution- A Modern Approach To Timeless Celebration"

"Event Evolution- A Modern Approach To Timeless Celebration"

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Bhawanani, Meher Sachadeo, Khushbu Meshram, Komal Marsettiwwar, Rounit Rawat, Mayur Sarode, Pranali Jhulelal Institute of Technology Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9798350348460

The event management system is a valuable tool that allows organizations to effectively maintain client engagement, even when clients are on the move. In today's fast-paced and technologically advanced world, the online platform has emerged as the preferred medium for accessing various services. Recognizing this trend and the need to adapt to changing consumer behavior, this proposal aims to develop a comprehensive event management system that encompasses the entire process of planning and coordinating *** planning involves a multitude of tasks and responsibilities, all of which need to be carefully managed to ensure the success of an event. These tasks include budgeting, scheduling, event selection, obtaining necessary permits, coordinating transportation and parking, arranging for speakers or entertainers, organizing decor, ensuring event security, managing catering services, collaborating with third-party vendors, and establishing emergency plans. All of these requirements will be taken into consideration during the design and development of the event management system. © 2024 IEEE.

关键词： budgeting event management system event selection transportation

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An ensemble learning approach for large scale birds species classification

An ensemble learning approach for large scale birds species ...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Vardhan, Harsh Verma, Aryan Singh, Nagendra Pratap Department of Computer Science and Engineering National Institute of Technology Hamirpur India

ISBN: (纸本)9781032669663

Birds are vertebrate animals that are adapted for flight due to the presence of hollow bone structures. The entire population of birds contributes 0.08 % to the total animal biomass. In the past two decades, there has been a continuous loss and degradation of natural habitats resulting in a threat to bird population survival. The United Nations calculates that 49% of the bird population is declining, and some 1500 species have already gone extinct in the last 100 years. Researchers are studying the behavior and morphological characteristics of different bird species to understand them so that necessary steps can be taken for their protection. It is evinced that manually classifying bird species is a very inefficient and time-consuming task. Through the use of Automatic Bird Species Classification, this time can be reduced from hours to minutes. This paper features an automatic bird species classification system utilizing an ensemble of deep neural networks. Our proposed method trains individual state-of-the-art architectures like VGG 19, DenseNet 201, and ViT to classify 400 bird species. Further, the performance of these models is evaluated using metrics like F1 scores, precision, and recall. Our developed ensemble is better generalized and adapted to the problem with excellent accuracy of 99.40%. Results have stated that our approach is notably much better than existing works on bird species classification. © 2024 The Author(s).

关键词： Image classification

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SecureHD: Designing Low-Cost Reliable and Security Aware Hardware Accelerators During High-Level Synthesis for Computationally Intensive Application Frameworks 10

SecureHD: Designing Low-Cost Reliable and Security Aware Har...

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

Reliable and low-cost hardware accelerator design and its security countermeasures are crucial to safeguard not only the end consumer but also the intellectual property (IP) vendor and system-on-chip (SoC) integrator. The reusable hardware accelerator-based design paradigm has become popular for its usage in several consumer electronics (CE) and computing systems. This is because the usage of hardware accelerators enables higher performance and efficacy by accelerating the underlying computationally-intensive application frameworks. In the proposed work, secure hardware accelerators for digital signal processing (DSP), multimedia and machine learning frameworks are designed (at zero design overhead) during high-level synthesis (HLS) as it offers lesser design complexity than lower levels of chip design. Further, HLS facilitates to integration of modules for design space exploration and integrating multi-layer security. The proposed methodologies offer the following: (a) hardware accelerator design for various computationally intensive application frameworks (b) negligible design cost overhead (c) seamless detective control against counterfeiting (d) higher tamper tolerance (e) low probability of false positive (f) higher strength of obfuscation to thwart reverse engineering attack. © 2024 IEEE.

关键词： High level synthesis

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MediMind: A Comprehensive Health Prediction and Record-Keeping Platform 6th

MediMind: A Comprehensive Health Prediction and Record-Keep...

引用

6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Naik, Darshana A. Sharma, Aditya Mahajan, Danish Khandelwal, Dhruv Gajya, Arpit Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9783031707889

The "MediMind" project signifies a revolutionary fusion of predictive analytics, centered on diabetes and heart disease risk assessment, coupled with state-of-the-art skin disease prediction and robust Electronic Health Record (EHR) management. By employing advanced machine learning models like Logistic Regression and Support Vector Machines (SVM) for diabetes and heart disease predictions, alongside the cutting-edge Convolutional Neural Network (ResNet-50) for precise skin disease classification, "MediMind" achieves unparalleled accuracy and applicability. The predictive models exhibit exceptional performance metrics, boasting high accuracy, precision, recall, and F1-score. Seamless integration into Electronic Health Records (EHR) ensures immediate implementation in clinical decision-making, providing healthcare professionals with precise insights into diabetes and heart disease risks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneous Links 16

Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneo...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Gumasthi, Nikhil B. Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Modern cellular devices often have dual interfaces for providing high-speed data transfer. This dual-interface design enhances versatility and user convenience. Dual interface technology allows 5G NR devices to connect to the 5G core using two separate base stations. One base station operates in the mmWave frequency range, and the other operates in the sub-6 GHz frequency range. However, millimeter-wave (mmWave) signals face challenges in terms of limited penetration through obstacles, such as buildings, and susceptibility to atmospheric absorption, leading to reduced coverage and potential signal degradation in adverse weather conditions. On the other hand, sub-6 GHz waves offer better penetration through obstacles and a broader scope compared to mmWave, making them suitable for providing reliable and expansive wireless connectivity in urban and indoor environments. Both mmWave and sub-6 GHz frequencies have distinct advantages and limitations;mmWave provides high data rates but has limited coverage, while sub-6 GHz offers a broader range but at lower data rates. However, in a heavy data transfer scenario, frequent loss in the mmWave link while transmitting packets can lead to packet drops (wireless medium), reducing the congestion window size and increasing the flow completion time. To mitigate this, we propose to use analytical hierarchy process (AHP) and grey rational analysis (GRA) based priority allocation mechanism to schedule packets on the most reliable link, thereby reducing the packet drops and flow completion time. © 2024 IEEE.

关键词： 5G NR Multipath TCP (MPTCP) network simulator-3 (NS-3) network simulator-3-mmWave priority scheduling

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Interpreting Fake Reviews Using Machine Learning and Deep Learning

Interpreting Fake Reviews Using Machine Learning and Deep Le...

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Bhat, Mohammad Qazim Jayalakshmi, D.S. Mallegowda, M. Geetha, J. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819983452

The issue of fake reviews on online platforms has increasingly become a huge concern in recent years, with the potential to mislead consumers and negatively impact businesses. In this paper, we present a comprehensive approach to detecting fake reviews using both supervised and unsupervised learning techniques. Our approach includes classic machine learning algorithms, deep learning techniques such as RNN and attention networks, as well as state-of-the-art models like BERT and GPT. We leverage a labeled dataset of restaurant reviews from *** to train and evaluate our models. We also compare the performance of supervised and unsupervised learning techniques and identify the most effective and explainable models for detecting fake reviews. Our results show that our approach achieves high accuracy in detecting fake reviews, and the interpretation of our models offers valuable insights into the factors that contribute to the identification of fake reviews. We believe our work contributes to the ongoing effort of combating fake reviews and provides a practical and effective solution for businesses and consumers to identify trustworthy reviews. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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